Abstract
With the increasing pressure from the market and the surge of “Industry 4.0,” staying competitive and relevant is becoming more and more difficult. The assembly line, which represents a long-term investment of the manufacturing industry, needs to be efficiently utilized. While assembly line balancing (ALB) problem had been studied for decades, oversights on the bottleneck resources could significantly impede its efficiency. In leveraging such information as part of the optimization problem, a contagious artificial immune network (CAIN) approach is proposed to simultaneously address ALB efficiency and bottleneck resources while achieving a truly balanced production line. A computational experiment conducted on benchmark data sets has demonstrated a proof-of-concept, where leveraging knowledge-intensive optimization approach had successfully produced high-quality solutions up to 100% improvement with statistically significant justification. Such findings may play an essential determinant in the manufacturing industry, whether being relevant or left out in the era of increasingly being information-driven.
Highlights
T HE manufacturing industry had recently undergone a significant surge of “Industry 4.0” which offers a plethora of technological innovations that leverage several key enabling technologies such as internet-of-things (IoT), cyber-physical system (CPS), collaborative robots (Cobots), machine learning (ML), Big data, augmented reality, cloud computing, and additive manufacturing [1]
The assembly line balancing (ALB) problem underwent a great deal of research, which had been shown by the vast numbers of approaches through a wide range of applications
This study focuses on enhancements of the combination of bone marrow (BM), artificial immune network (AIN), and CSA models, to address the straight assembly line balancing (SALB)-E problem with shifting bottleneck
Summary
T HE manufacturing industry had recently undergone a significant surge of “Industry 4.0” which offers a plethora of technological innovations that leverage several key enabling technologies such as internet-of-things (IoT), cyber-physical system (CPS), collaborative robots (Cobots), machine learning (ML), Big data, augmented reality, cloud computing, and additive manufacturing [1]. Measuring the Type-E SALB problem (SALB-E) would determine the total productive portion of the assembly line, where significant capital costs (number of machines) and minimal demand lead time (cycle time) were addressed simultaneously This objective is scarcely adopted due to its non-linearity, making it more challenging to deal with [2], [3]. A knowledgeintensive optimization algorithm is suited for addressing the SALB-E through problem-specific knowledge of the shifting bottleneck identification An example of such an approach is the artificial immune system (AIS) algorithm, where its potentials have been widely utilized in a variety of domain problems.
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